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Course Outline
Introduction to AI in Supply Chain and Logistics
- Emerging trends in smart logistics
- Comparing AI with traditional analytics in supply chain management
- Core technologies and relevant platforms
AI-Driven Demand Forecasting
- Implementing time-series forecasting using machine learning
- Managing seasonality and trend components effectively
- Enhancing forecast accuracy through historical data analysis
Optimizing Inventory and Replenishment
- Predicting stock levels with AI precision
- Calculating safety stock and optimal reorder points
- Integrating AI solutions with ERP and WMS systems
Route Optimization and Fleet Intelligence
- Applying shortest path algorithms for delivery routing
- Dynamic route planning with traffic awareness
- Scheduling transport operations using AI capabilities
Warehouse Automation and Robotics
- Utilizing AI for picking, sorting, and storage automation
- Employing computer vision for shelf monitoring
- Coordinating operations with AGVs and robotic arms
Real-Time Analytics and Dashboard Development
- Creating live dashboards using Tableau and Python
- Tracking KPIs through real-time data streams
- Setting up alerts and handling operational exceptions
Case Studies and Capstone Project
- Evaluating complex multi-node supply chain scenarios
- Applying forecasting and routing models in practice
- Presenting a comprehensive, data-driven logistics optimization strategy
Conclusion and Future Pathways
Requirements
- Foundational knowledge of supply chain or logistics operations
- Practical experience with data analysis or business intelligence platforms
- Basic proficiency in programming or scripting languages
Target Audience
- Supply chain analysts
- Logistics managers
- Industrial planners
21 Hours
Testimonials (1)
The input fm other industries through the trainer.
